Weightless Neural Models: An Overview

Ludermir, Teresa Bernarda

doi:10.1007/978-3-030-79092-9_15

Cited by 1 publication

(1 citation statement)

References 33 publications

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“…Weightless neural networks (WNNs) have emerged as a game-changing answer to the challenges of merging neural network techniques with low-power hardware devices, which are usually constrained by memory, power, and performance restrictions [14]. Bledsoe and Browning introduced WNNs in 1959, which was a signifcant development in hardware-optimized neural network approaches.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Tire Condition Monitoring through Weightless Neural Networks Using MEMS-Based Vibration Signals

Arora,

Naveen Venkatesh,

Sugumaran

et al. 2024

Journal of Engineering

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Tire pressure monitoring system (TPMS) has a critical role in safeguarding vehicle safety by monitoring tire pressure levels. Keeping the accurate tire pressure is necessary for confirming comfortable driving and safety, and improving fuel consumption. Tire problems can result from various factors, such as road surface conditions, weather changes, and driving activities, emphasizing the importance of systematic tire checks. This study presents a novel method for tire condition monitoring using weightless neural networks (WNN), which mimic neural processes using random-access memory (RAM) components, supporting fast and precise training. Wilkes, Stonham, and Aleksander Recognition Device (WiSARD), a type of WNN, stands out for its capability in classification and pattern recognition, gaining from its ability to avoid repetitive training and residual formation. For vibration data acquisition from tires, cost-effective micro-electro-mechanical system (MEMS) sensors are employed, offering a more economical solution than piezoelectric sensors. This approach yields a variety of features, such as autoregressive moving average (ARMA), statistical and histogram features. The J48 decision tree algorithm plays a critical role in selecting essential features for classification, which are subsequently divided into training and testing sets, crucial for assessing the WiSARD classifier’s efficacy. Hyperparameter optimization of the WNN leads to improved classification accuracy and shorter computation times. In practical tests, the WiSARD classifier, when optimally configured, achieved an impressive 97.92% accuracy with histogram features in only 0.008 seconds, showcasing the capability of WNN to enhance tire technology and the accuracy and efficiency of tire monitoring and maintenance.

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Section: Introductionmentioning

confidence: 99%